Build and operate a scalable data platform ingesting real-time events with a high-throughput rate.
Collaborate with Data Scientists to transition ML models from experimentation to production
Build and maintain ML infrastructure for model serving (using FastAPI) and track model performance and lifecycle over time
Collaborate with analysts, engineers and product managers to understand user needs and take ownership of producing new event tracking functionality.
Implement automations, high data quality controls, ensure data integrity in the ingested data, and create necessary monitoring alerts.

Experience deploying and maintaining Python services in a major cloud environment (AWS, GCP, Azure).
Specific experience with the AWS stack, including Kinesis, Lambdas, Glue, Firehose, and Athena, is highly desirable.
Experience with MLOps frameworks and experiment tracking tools such as AWS Sagemaker, MLFlow, Databricks, or W&B
Basic knowledge of ML Inference REST APIs (FastAPI)
Great skills improving and operating Schema Registries and Data Catalogs (Glue, Databricks, etc.)
Relevant CI/CD experience (e.g., GitHub Actions, Gitlab Pipelines) automating tests and updates in schema registries and lambda releases.
Solid experience with SQL and data warehousing, data lake environments (e.g. DataBricks, BigQuery, Redshift, S3).
Familiarity with cloud observability tools (e.g. Datadog, NewRelic, or Cloudwatch).

MLOps Engineer

Key skills